Method and device for processing sar raw data

ABSTRACT

A method according to the present invention comprises the steps of: dividing SAR raw data into one or more sub-aperture data by a predetermined number in an azimuth direction; performing a spectral length extension FFT on the sub-aperture data in the azimuth direction; multiplying the sub-aperture data by a chirp scaling function; performing a range FFT on the sub-aperture data; performing range compression, SRC, and a bulk RCMC on the sub-aperture data; performing an inverse chirp-z transform on the sub-aperture data in a range direction; multiplying the divided sub-aperture data by a predetermined first function; performing an IFFT on the sub-aperture data in the azimuth direction; recombining the sub-aperture data; multiplying the recombined data by a second function and deramping same; performing an azimuth FFT on the recombined data; performing an azimuth IFFT on the recombined data; multiplying the recombined data by a third function and deramping same; performing the azimuth FFT on the recombined data; performing azimuth compression by multiplying the recombined data by a fourth function; performing an azimuth inverse chirp-z transform on the recombined data; and multiplying the recombined data by a fifth function for phase preservation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority from Korean Patent Application No.10-2018-0163257, filed on Dec. 17, 2018 in the Korean IntellectualProperty Office, the invention of which is incorporated herein byreference in its entirety.

BACKGROUND Technical Field

The present invention relates to a method for generating single lookcomplex (SLC) data by processing synthetic aperture radar image (SAR)raw data.

Background Art

The synthetic aperture radar image (SAR) system consists of a hardwarepayload (payload-sensor), a bus (satellite) and a software terrestrialimage processing system. The output of the SAR system is atwo-dimensional SAR image product for a desired area, and the betterquality of this data can lead to an increased usefulness of additionalinformation obtained therefrom. In order to generate a high-quality SARvideo product, it is possible to increase the investment in hardwareperformance or design, but in this case, a considerable cost isrequired.

The SAR raw data obtained by the SAR satellite in any observation modeare two-dimensional data including the real part and the imaginary part,which are information data of the complex number. The SAR satelliteperforms observation by side looking. The flight direction of thesatellite is called the azimuth or along-track. In addition, thedirection the satellite's antenna is facing is called the range orcross-track. The SAR raw data are two-dimensional data formed in azimuthand range direction.

In general, the main function of the SAR processing (SARP) corealgorithm is to form an image from the SAR raw data as illustrated inFIG. 1.

FIG. 1 is a diagram illustrating an image formation process of the SARPcore algorithm.

In the SARP core algorithm, focusing corresponds to a process oftransforming the mathematical model of the raw data for any point targetwithin the observed area and forming two-dimensional image informationabout the point target.

FIG. 2 is an example of representing SARP focusing by mathematicalmodeling.

In FIG. 2, η is an azimuth time, τ is a range time, c is a luminous flux(m/sec), and η_(c) is an azimuth time at which the beam center crossesthe point target, f₀ is a carrier frequency, R is a slant range, andK_(r) is a chirp rate.

FIG. 3 is a diagram illustrating a focusing process of the SARP corealgorithm.

As illustrated in FIG. 3, the process of focusing raw data on any pointtarget is performed through processes of {circle around (1)} rangecompression (i.e., compression of SAR raw data in the range direction),{circle around (2)} range interpolation (i.e., interpolation in therange direction), and {circle around (3)} azimuth compression (i.e.,compression in the azimuth direction).

The baseband azimuth scaling algorithm (BAS) [Paper 1: Pau Prats,Member, IEEE, Rolf Scheiber, Josef Mittermayer, Member, IEEE, AdrianoMeta, Member, IEEE, and Alberto Moreira, Fellow, IEEE, “Processing ofSliding Spotlight and TOPS SAR Data Using Baseband Azimuth Scaling,”IEEE Trans. Geosci. Remote Sens., vol. 48, no. 2, pp. 770-780, February2010.], known as one of the most advanced technologies amongconventional SARP core algorithms, also performs the SAR raw dataprocessing as in the general SARP core algorithm illustrated in FIG. 2.

For the range direction processing (range cell migration correction,secondary range compression, and range compression), the BAS applies theECS algorithm method in [A. Moreira, J. Mittermayer, and R. Scheiber,“Extended chirp scaling algorithm for air- and spaceborne SAR dataprocessing in stripmap and ScanSAR imaging modes,” IEEE Trans. Geosci.Remote Sens., vol. 34, no. 5, pp. 1123-1136, September 1996], which isthe reference [6] of Paper 1, and may use the corresponding part of anycommonly known SARP core algorithm.

The processes and characteristics of the conventional BAS processingwill be described in detail.

The BAS suggests the technical method of azimuth processing, and therange processing applies the same method as the conventional ECS.

FIG. 4 is a diagram showing a process of Sliding spotlight and TOPS SARdata processing using the conventional BAS.

Referring to FIG. 4, the baseband azimuth scaling (BAS) includes H₄, H₅,H₆ and H₇ of the SARP core algorithm. The key points are H₄ and H₅. Thisis because H₆ and H₇ are the remaining components formed from naturalmathematical evolution by H₄ and H₅.

${H_{4}\left( {f_{a},r} \right)} = {{\exp\left\lbrack {j\frac{4\;\pi}{\lambda}{r \cdot \left( {{\beta\left( {f_{a},r} \right)} - 1} \right)}} \right\rbrack} \cdot {\exp\left\lbrack {{- j}\; 2\pi\; f_{a}{t_{v}(r)}} \right\rbrack} \cdot {\exp\left\lbrack {{- j}\frac{\pi}{K_{{sc}\; 1}(r)}f_{a}^{2}} \right\rbrack}}$

λ: wavelength

r: closest approach range

f_(a): azimuth frequency (Doppler frequency shift)

${\beta\left( {f_{a},r} \right)} = \sqrt{1 - \left( \frac{\lambda\; f_{a}}{2{v_{eff}(r)}} \right)^{z}}$

v_(eff): effective velocity

t_(v)(r): time shift

${K_{scl}(r)} = {{- \frac{2{v_{eff}^{2}\left( r_{mid} \right)}}{\lambda\;{r_{scl}(r)}}}:}$

scaling Doppler rate

${r_{scl}(r)} = {\frac{r_{{scl}\; 0}}{r_{{ror}\; 0}}{r_{rot}(r)}}$${r_{rot}(r)} = \frac{r_{{rot}\; 0} - r}{1 - \frac{r_{{scl}\; 0}}{r_{{rot}\; 0}}}$

r_(scl0): scaling range selected for controlling azimuth image sampling

r_(rot0): distance to the beam rotation center given by the geometry

H ₅(t _(a) ,r)=exp[−jπK _(rot)(r)·(t _(a) −t _(mid))²]

t_(a): azimuth time

${K_{rot}(r)} = {{- \frac{2{v_{eff}^{2}\left( r_{mid} \right)}}{\lambda\;{r_{rot}(r)}}}:}$

azimuth derotation Doppler rate

t_(mid): selected derotation center azimuth time

${H_{6}\left( {f_{a},r} \right)} = {{w\left( f_{a} \right)} \cdot {\exp\left\lbrack {j\frac{\pi}{K_{eff}(r)}f_{a}^{2}} \right\rbrack}}$K_(eff)(r) = K_(scl)(r) − K_(rot)(r)${H_{7}\left( {t_{a},r} \right)} = {\exp\left\lbrack {j\;\pi\;{{K_{t}(r)} \cdot \left( {1 - \frac{r_{{scl}\; 0}}{r_{{rot}\; 0}}} \right)^{2} \cdot \left( {t_{a} - t_{mid}} \right)^{2}}} \right\rbrack}$${K_{t}(r)} = {- \frac{2{v_{eff}^{2}\left( r_{mid} \right)}}{\lambda \cdot \left( \;{{r_{rot}(r)} - {r_{scl}(r)}} \right)}}$

The conventional BAS has advantages and characteristics of setting adesired value of the azimuth sample spacings of the image whileperforming proper derotation, by setting r_(scl)(r) and r_(rot)(r) asdescribed above.

The azimuth sample spacings of the image are processed by the BAS asfollows.

Δx _(final) =Δx _(original)·(1−r _(scl0) /r _(rot0))

Δx_(final): azimuth sample spacings after processing

Δx_(original): azimuth sample spacings before processing

Meanwhile, the conventional BAS has the following constraints by thecore technical components of the algorithm. These constraints make itunsuitable for the processing of data obtained by the operation in thestaring spotlight mode and by the operation in the sliding spotlightmode close to the operation in the staring spotlight mode.

Basically, the SARP core algorithm should perform signal processingwhile satisfying the Nyquist criteria. Otherwise, image distortionoccurs. Further, a proper sample spacings compared to the resolutionshould be set. Otherwise, the efficiency is lowered, and the SARP corealgorithm cannot be adopted according to the requiring response time ofthe SAR system.

There are constraints that the BAS has to overcome in order to functionproperly: First, the azimuth bandwidth after derotation should satisfythe Nyquist criteria. Second, the Doppler rate after derotation shouldhave a proper large value other than ‘0’. Third, the azimuth pixelspacings of the processed image should not be too small compared to theazimuth resolution. Fourth, after processing, the azimuth time range ofthe azimuth scene should not be increased too much. However, for theiroperation purpose and characteristics, ScanSAR and TOPS modes do notapply these constraints. Fifth, the time shift of the azimuth signal byH₄ should not be too large.

Since the first and second constraints are related with the accuracy ofimage processing, they should always be met. The third to fifthconstraints are related with efficiency and processing speed.

Conditions for overcoming the constraints of the BAS may be specifiedand set as follows. In order to avoid the first, second, and thirdconstraints, Paper 1 sets out that the following conditions should bemet.

${r_{{rot}\; 0} \geq \frac{r_{scl}}{1 - {\gamma\frac{r\;\theta_{az}}{v_{g}T_{obs}}}}},{\gamma \geq 0.8}$

v_(g): ground beam velocity

T_(obs): observation duration

θ_(az): azimuth antenna beamwidth

The above may be rewritten as follows.

${{\frac{r_{{rot}\; 0} - r_{{scl}\; 0}}{r_{{rot}\; 0}}} \leq {\gamma \cdot \frac{r\;\theta_{az}}{v_{g}T_{obs}}}},{1 > \gamma \geq 0.8}$

Since the

$\frac{r_{{rot}\; 0}}{r_{{rot}\; 0} - r_{{scl}\; 0}}$

value should not be large in order to overcome the fourth constraint,the following condition may be set.

${{{\frac{r_{{rot}\; 0}}{r_{{rot}\; 0} - r_{{scl}\; 0}}} \cdot \Delta}\; t_{a\; 0}} \leq {1.25 \cdot T_{a}}$

T_(a): total observation time

Δt_(a0): actual length of azimuth time in the scene

However, for their operation purpose and characteristics, ScanSAR andTOPS modes do not apply these constraints.

Since the

${t_{p}(r)} = {f_{a} \cdot \left\{ \frac{- {\lambda\left( {{r_{sel}(r)} - r} \right)}}{2{v_{eff}^{2}\left( r_{mid} \right)}} \right\}}$

value should not be large in order to overcome the fifth constraint, thefollowing condition may be set.

That is,

${{t_{v}(r)}} = {{{f_{a} \cdot \left\{ \frac{{- \lambda} \cdot \left\{ \frac{r_{{rot}\; 0}\left( {r_{{scl}\; 0} - r} \right)}{r_{{rot}\; 0} - r_{{scl}\; 0}} \right\}}{2{v_{eff}^{2}\left( r_{mid} \right)}} \right\}}} \leq {0.025 \cdot {T_{a}.}}}$

As discussed above, the first, second, and third conditions have anopposite relationship with the fourth and fifth conditions.

In the conventional BAS, when r_(rot0) is set according to theobservation geometry, there exist the constraints on r_(scl0) listedabove. In addition, when image processing is performed onmulti-subswaths, r_(scl0) values of the subswaths should be set to havea certain ratio between them in order to match the azimuth pixelspacings between subswaths. As mentioned in Paper 1, when it comes tothe staring spotlight mode and the sliding spotlight mode close to themode operation of the staring spotlight mode, and also the SAR systemshaving specific performance, it may be difficult to select the BAS asthe SARP core algorithm because the BAS does not satisfy thoseconstraints. Further, for the high-resolution SAR system, when theazimuth matched filter of H₄ is used as it is, the focusing accuracy maybe lowered. Therefore, the SARP core algorithm of the present inventionis required, which overcomes all of the above constraints and issuitable for the high performance SAR system.

SUMMARY Technical Problem

Therefore, the technical problem to be solved by the present inventionis to provide a method for processing SAR raw data according to SARPcore algorithm applicable to all of the SAR operational modes includingStripmap, ScanSAR, TOPS, sliding spotlight, staring spotlight, andoperational modes between the sliding spotlight and the staringspotlight.

Technical Solution

In order to solve the technical problems mentioned above, a method forprocessing SAR raw data according to the present invention is provided,which may include dividing synthetic aperture radar image (SAR) raw datainto one or more sub-aperture data by a predetermined number in anazimuth direction, performing a spectral length extension fast Fouriertransform (FFT) on the divided sub-aperture data in the azimuthdirection, multiplying the divided sub-aperture data by a chirp scalingfunction, performing the FFT on the divided sub-aperture data in a rangedirection, performing a range compression, a secondary range compression(SRC), and a bulk range cell migration correction (RCMC) on the dividedsub-aperture data, performing an inverse chirp-z transform on thedivided sub-aperture data in the range direction, multiplying thedivided sub-aperture data by a first function predetermined for residualphase correction and azimuth scaling, performing an inverse fast Fouriertransform (IFFT) on the divided sub-aperture data in the azimuthdirection, recombining the divided sub-aperture data, multiplying therecombined data by a second function to perform deramping, performingthe FFT on the recombined data in the azimuth direction, performing theIFFT on the recombined data in the azimuth direction, multiplying therecombined data by a third function to perform deramping, performing theFFT on the recombined data in the azimuth direction, performing anazimuth compression by multiplying the recombined data by a fourthfunction, performing the inverse chirp-z transform in an azimuthdirection on the recombined data, and multiplying the recombined data bya fifth function for phase preservation.

Further, in order to solve the technical problems mentioned above, acomputer-readable recording medium storing a program for performing amethod for processing the SAR raw data according to the presentinvention may be provided.

The program may include a plurality of instruction sets for performingthe method for processing the SAR raw data described above.

Advantageous Effects

According to the present invention, there may be provided a method forprocessing SAR raw data according to SARP core algorithm applicable toall of the SAR operational modes including Stripmap, ScanSAR, TOPS,sliding spotlight, staring spotlight, and operational modes between thesliding spotlight and the staring spotlight.

BRIEF DESCRIPTION OF THE DRAWING

FIG. 1 is a diagram illustrating an image formation process of a SARPcore algorithm.

FIG. 2 is an example of representing SARP focusing by mathematicalmodeling.

FIG. 3 is a diagram illustrating a focusing process of the SARP corealgorithm.

FIG. 4 is a diagram showing a process of Sliding spotlight and TOPS SARdata processing using the conventional BAS.

FIG. 5 is a diagram provided to explain a SARP core algorithm accordingto an embodiment of the present invention.

DETAILED DESCRIPTION

Hereinafter, the exemplary embodiments of the present invention will bedescribed in detail with reference to the accompanying drawings forthose with ordinary knowledge in the art to be able to easily achievethe present invention.

FIG. 5 is a diagram provided to explain a method for processing SAR rawdata by a SARP core algorithm according to an embodiment of the presentinvention.

Referring to FIG. 5, first, a processor for processing the SAR raw dataaccording to the present invention divides the synthetic aperture radarimage (SAR) raw data into one or more sub-aperture data by apredetermined number in an azimuth direction, at S1.

When dividing and processing the SAR raw data based on a sub-apertureunit in the azimuth direction at S1, it is possible that the azimuthtime length of one sub-aperture may be set as any value withoutlimitations within the azimuth time length of the entire raw data. Thisis because the method for converting the data into the azimuth frequencydomain while satisfying the Nyquist criteria is not a short azimuth FFTbut a spectral length extension azimuth FFT. The number of sub-aperturesmay be set in consideration of the beam or operational mode of thedesigned SAR system. At S1, the number of sub-apertures may be set to aminimum value of 1 or 2 or more. The azimuth time length of thesub-aperture is determined according to the set number of sub-apertures.

When the short azimuth FFT is used, the processing accuracy of the SARPmay be lowered for a certain SAR system. This is because the algorithmof the SARP is developed in the principle of stationary phase (POSP)manner and thus there is an error in approximation, and the error isincreased as the time bandwidth product (TBP) value is decreased, andthe short FFT decreases the TBP value. In addition, the short azimuthFFT method increases the number of sub-apertures, resulting in loweredquality of the image in a part where the sub-aperture images arerecombined. On the other hand, the SARP core algorithm according to thepresent invention minimizes the number of sub-apertures while allowingadjustment, thereby ensuring maximum accuracy in the process ofprocessing the signal for any SAR system. The shortcoming of this methodcan be that the processing time is increased due to the additionalspectral length extension process, but this is acceptable whenconsidering that the method can be applied to the entire operationalmodes.

Next, at S2, the spectral length extension azimuth FFT may be performedon the sub-aperture data divided at S1. Through S2, the sub-aperturedata may be converted from the SAR signal domain into the azimuthfrequency domain.

After performing S2, chirp scaling may be performed by multiplying thesub-aperture data converted into the frequency domain by a chirp scalingfunction (H_(cs)), at S3.

After performing S3, a range FFT may be performed on the dividedsub-aperture data in a range direction, at S4. The sub-aperture datadivided through S4 may be converted into a two-dimensional frequencydomain.

After performing S4, by multiplying the sub-aperture data converted intothe two-dimensional frequency domain by a function H_(RC)×H_(RC)×H_(BV)at S5, the range compression, the secondary range compression (SRC), andthe bulk range cell migration correction (RCMC) may be performed on thedivided sub-aperture data. H_(RC) is a function to perform the rangecompression, H_(RC) is a function to perform the SRC, and H_(BV) is afunction to perform the bulk RCMC. According to an embodiment, the rangecompression, the secondary range compression (SRC), and the bulk rangecell migration correction (RCMC) may be performed.

First, H_(cs), H_(RC), H_(RC) and H_(BV) may use a functioncorresponding to any generally known SARP core algorithm. For example,the functions proposed in “Ian G. Cumming, Frank H. Wong, DigitalProcessing of Synthetic Aperture Radar Data, Artech House Inc., pp.283-322, 2005.” may be used, the details of which are well known to aperson skilled in the art and thus will not be redundantly described indetail herein.

Next, after performing S5, an inverse chirp-z transform may be performedon the divided sub-aperture data in the range direction, at S6. As amethod for forming an image at S6, inverse chirp-z transformation (ICZT)may be used instead of the inverse FFT (IFFT). The method of the IFFTsets the sample spacings of an image by adjusting the number of samplesof data in the frequency domain. However, in the IFFT method, since thenumber of samples is integer and can only be set discontinuously, thereis a constraint that the sample spacings are also adjusteddiscontinuously. In the case of the SAR operational mode in whichobservation is performed with multiple beams, there is a highpossibility that the sample spacings between formed images of the beamsdiffers even by a very small value. When such images of multiple beamsare simply mosaicked, the quality of the entire scene image is lowered.As the image size becomes larger, the distortion of the locationinformation becomes greater. When the interpolation function is appliedto solve this problem, the quality of the image is lowered and theprocessing time is increased due to the accuracy error of theinterpolation itself. Therefore, according to an embodiment, by usingthe inverse chirp-z transform method capable of continuously adjustingthe sample spacings, the accuracy of the positions or values of thepixels during image formation may be secured to the maximum for any SARsystem.

At S6, the inverse chirp-z transform may be performed by Equation 1below, and may perform a function of inverse transforming any inputsignal in the frequency domain into a signal in the time domain.

$\begin{matrix}{{{x_{k} = {W^{\frac{k^{2}}{2}} \cdot \left\lbrack {{FFT}\left\{ {{IFFT}{\left\{ Y_{n} \right\} \cdot {IFFT}}\left\{ W^{- \frac{n^{2}}{2}} \right\}} \right\}} \right\rbrack}},{k = 0},1,\ldots\mspace{14mu},{M - 1}}{{Y_{a} = {{X\left( z_{n} \right)} \cdot B^{n} \cdot W^{\frac{n^{2}}{2}}}},{n = \frac{F_{n}}{\Delta\; F}}}{B = {{B_{0} \cdot {\exp\left( {j\; 2\pi\;\theta_{0}} \right)} \cdot \theta_{0}} = {\Delta\;{F \cdot t_{0}}}}}{W = {{W_{0} \cdot {\exp\left( {j\; 2\;{\pi\phi}_{0}} \right)} \cdot \phi_{0}} = {\Delta\;{F \cdot \Delta}\; t}}}} & \left\lbrack {{Equation}\mspace{14mu} 1} \right\rbrack\end{matrix}$

where, X(z_(n)) is a signal in the input frequency domain, M is thenumber of output sample signals, ΔF is frequency spacings of the inputspectrum signals, and B₀ and W₀ are amplitude constants. The start timeof the signal x_(k) on the output time is set to t₀, and the timespacing of the samples is set to Δt.

Next, at S7, with respect to the inverse chirp-z transformedsub-aperture data at S6, the predetermined first function H₄ for theresidual phase correction and azimuth scaling may be multiplied by thesub-aperture data that is inverse chirp-z transformed in the rangedirection at S6.

At S7, accurate azimuth matched filtering is performed on thesub-aperture data inverse chirp-z transformed in the range direction bythe first function H₄, and a quadratic phase signal is formed by usingK_(scl)(r) corresponding to the actual azimuth Doppler rate component.

Next, at S8, the inverse fast Fourier transform (IFFT) may be performedon the divided sub-aperture data in the azimuth direction. Through S8,the sub-aperture data may be converted from the range Doppler domaininto the SAR signal domain.

After S1 to S8 are all performed on the sub-aperture data divided fromthe SAR raw data, the divided sub-aperture data may be recombined at S9.When the SAR raw data is processed as one sub-aperture data at S1, S1 toS8 may be performed only once.

Thereafter, the data recombined at S9 (hereinafter, “recombined data”)may be multiplied by a second function H₅ to perform deramping at S10.

Next, at S11, FFT may be performed on the deramped recombined data atS10 in the azimuth direction, and azimuth antenna pattern compensationmay be performed.

Next, at S12, IFFT may be performed in the azimuth direction on theFFT-processed recombined data in the azimuth direction at S11.

Thereafter, the IFFT-processed recombined data in the azimuth directionat S12 may be multiplied by a third function H₆ to perform deramping atS13.

Then, at S14, the FFT may be performed in the azimuth direction on thederamped recombined data at S13.

Next, at S15, the azimuth compression (AC) may be performed bymultiplying the FFT-processed recombined data in the azimuth directionat S14 by a fourth function H₇.

Thereafter, at S16, the inverse chirp-z transform in the azimuthdirection may be performed on the recombined data compressed in theazimuth direction at S16.

Finally, at S17, the inverse chirp-z transformed recombined data in theazimuth direction at S16 may be multiplied by a fifth function H₈ forphase preservation to generate single look complex (SLC) data.

Hereinbelow, the first function H₄, the second function H₅, the thirdfunction H₆, the fourth function H₇, and the fifth function H₈ used inthe SARP core algorithm according to the present invention and theconstraints to accurately process the SAR raw data in every SARoperational mode are described in detail.

The first function H₄ is defined by Equation 2, the second function H₅is defined by Equation 3, the third function H₆ is defined by Equation4, the fourth function H₇ is defined by Equation 5, and the fifthfunction H₈ is defined by Equation 6.

$\begin{matrix}{{H_{4}\left( {f_{a},r} \right)} = {{M_{1}\left( w_{\eta} \right)} \cdot {\exp\left\lbrack {{- j}\frac{\pi}{K_{scl}(r)}f_{a}^{2}} \right\rbrack}}} & \left\lbrack {{Equation}\mspace{20mu} 2} \right\rbrack\end{matrix}$

where,

${{K_{scl}(r)} = {- \frac{2\;{v_{eff}^{2}(r)}}{\lambda\;{r_{scl}(r)}}}},$r _(scl)(r)=r

M ₁(w _(η))=exp[j{2(2π/λ+w _(r) /c)R _(r2)(η*)+w _(η)η*}],

R _(r2)(η)=c ₄η⁴+(c ₃+4c ₄ t ₃)η³+(c ₂+3c ₃ t ₁+6c ₄ t ₁ ²)η²

where, λ is the wavelength with respect to the center frequency of thetransmission signal forming the beam, r is the closest approach range,f_(a) is the azimuth Frequency (Doppler Frequency shift), v_(eff) is theeffective velocity, K_(scl)(r) is a scaling Doppler rate, and c₂, c₃ andc₄ are coefficients. According to an embodiment, c₂, c₃ and c₄ areobtained by using geometry including orbit information and attitudeinformation of a satellite.

H ₅(t _(a) ,r)=exp[−jπK _(rot_geometry)·(t _(a) −t _(mid))²]  [Equation3]

where, t_(a) is the azimuth time, t_(mid) is the azimuth time of theselected derotation center, and r_(rot_geometry) is the distance to thebeam rotation center given by geometry.

$K_{{rot}\; 1} = {K_{rot\_ geometry} = {- \frac{2\;{v_{eff}^{2}(r)}}{\lambda\; r_{rot\_ geomtry}}}}$

is an azimuth derotation Doppler rate.

H ₆(t _(a) ,r)=exp[−jπ(K _(rot2)(r)−K _(rot_geometry))·(t _(a) −t_(mid))²]  [Equation 4]

${K_{{rot}\; 2}(r)} = {- \frac{2\;{v_{eff}^{2}(r)}}{\lambda\;{r_{{rot}\; 2}(r)}}}$

is the azimuth deramping Doppler rate, and r_(rot2)(r)=r·ε.

$\begin{matrix}{{{H_{7}\left( {f_{a},r} \right)} = {{W\left( f_{a} \right)} \cdot {\exp\left\lbrack {j\frac{\pi}{K_{eff}(r)}f_{a}^{2}} \right\rbrack}}}{{where},{{K_{eff}(r)} = {{K_{scl}(r)} - {K_{{rot}\; 2}(r)}}}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack \\{{{H_{8}\left( {t_{a},r} \right)} = {\exp\left\lbrack {j\;\pi\;{{K_{t}(r)} \cdot \left( {1 - \frac{1}{ɛ}} \right)^{2} \cdot \left( {t_{a} - t_{mid}} \right)^{2}}} \right\rbrack}}{{where},{{K_{t}(r)} = {- \frac{2\;{v_{eff}^{2}(r)}}{\lambda\; \cdot {r\left( {ɛ - 1} \right)}}}}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack\end{matrix}$

According to an embodiment, by adjusting ε, processing the SAR raw datausing the first function H₄, the second function H₅, the third functionH₆, the fourth function H₇, and the fifth function H₈ can be applied toall modes of the SAR system, i.e., stripmap, ScanSAR, TOPS, and slidingspotlight, staring spotlight, and any operational mode between slidingspotlight and staring spotlight.

Hereinbelow, a method for adjusting E will be described in detail.

Among the five constraints described with reference to the conventionalBAS system, some constraints are not applicable in the SARP corealgorithm according to the present invention. Specifically, since theSARP core algorithm of the present invention performs the azimuthspectral length extension FFT for each sub-aperture, the condition thatthe azimuth bandwidth after derotation should satisfy the Nyquistcriteria, which is the first constraint of the conventional BAS, is notapplicable. The constraint in which the Doppler rate after the secondderotation should have a proper large value other than ‘0’ isapplicable. In addition, since the pixel spacings may be freely adjustedby using ICZT instead of IFFT for image formation in the SARP corealgorithm according to the present invention, the condition that theazimuth pixel spacings of the processed image should not be too smallcompared to the azimuth resolution, which is the third constraint of theconventional BAS, is not applicable. The fourth constraint in which theazimuth time range of the azimuth scene should not be increased too muchis not applicable. Since the SARP core algorithm of the presentinvention leaves a quadratic component of the actual azimuth signal whenperforming azimuth scaling, the condition that the time shift of theazimuth signal by H₄ should not be too large, which is the fifthconstraint of the BAS, is not applicable.

Therefore, the constraints of the present invention may be simplified asfollows.

Firstly, the Doppler rate after the derotation should have a properlarge value other than ‘0’. Secondly, the azimuth time range of theazimuth scene should not be increased too much.

The azimuth bandwidth after deramping may be represented by Equation 7below.

$\begin{matrix}{B_{{a\_ Total}{\_ Span}} = {\frac{2\;{v_{eff}^{2}(r)}}{r \cdot \lambda} \cdot \left\{ {{{\left( {\frac{1}{ɛ} - \frac{r}{r_{rot\_ geometry}}} \right) \cdot T_{a}}} + {{\left( {1 - \frac{1}{ɛ}} \right) \cdot T_{{ab}\; s}}} + B_{FOV}} \right\}}} & \left\lbrack {{Equation}\mspace{14mu} 7} \right\rbrack\end{matrix}$

B_(FOV): Doppler frequency range of the data in instant field of view

$B_{FOV} = \frac{2 \cdot v_{a} \cdot \theta_{ax}}{\lambda}$

The first constraint according to the present invention described aboverequires that the second component

$\frac{2\;{v_{eff}^{2}(r)}}{r \cdot \lambda} \cdot {{\left( {1 - \frac{1}{ɛ}} \right) \cdot T_{{ab}\; s}}}$

in Equation 7 have a proper value greater than 0.

Further, the second constraint requires that the following conditions bemet.

${{{\frac{ɛ}{ɛ - 1}} \cdot \Delta}\; t_{a\; 0}} < {\gamma_{1} \cdot T_{a}}$

T_(a): total observation time

Δt_(a0): azimuth time length for scene size

γ₁>0γ₁: ratio of azimuth time range compared to Ta, on which scene wouldappear after application of H₇ and azimuth ICZT.

For example, γ=1.25 may be set. However, in the case of ScanSAR mode andTOPS mode, the second constraint may not be applied for the operationpurpose and characteristics.

Meanwhile, in order to satisfy the above two constraints, the SARP corealgorithm according to the present invention may set c appropriately foreach operational mode.

When the SAR operational mode is Stripmap, ScanSAR and TOPS mode,

$ɛ = \frac{r_{rot\_ geometry}}{r_{mid}}$

may be set.

However, when the SAR operational mode is Stripmap or ScanSAR, thefollowing may be set.

If the value of r_(rot_geometry) cannot be calculated numerically,r_(rot_geometry)=1000·r_(mid),

if |r_(rot_geometry)|>1000·r_(mid) and r_(rot_geometry)>0,r_(rot_geometry)=1000·r_(mid),

if |_(rot_geometry)|>1000·r_(mid) and r_(rot_geometry)<0,r_(rot_geometry)=−1000·r_(mid),

and if |r_(rot_geometry)|≤1000·r_(mid), the calculated r_(rot_geometry)value can be applied as is. Here, r_(mid) is the closest distance to thecenter of the scene.

Meanwhile, when the SAR operational mode is the spotlight operationalmode including the sliding spotlight and the staring spotlight, c may beset by the following equation.

$ɛ_{optimized} = \frac{ɛ_{{\min{\_\gamma}}_{1},\gamma_{2}} + ɛ_{{\max{\_\gamma}}_{1},\gamma_{2}}}{2}$

ε_(min_γ) ₁ _(,γ) ₂ , e_(max_γ) ₁ _(,γ) ₂ may be defined as an upperlimit and a lower limit of ε that satisfy both Conditional Equation 1and Conditional Equation 2 below.

ε_(min_γ) ₁ <ε<ε_(max_γ) ₁   [Conditional Equation 1]

For Conditional Equation 1, in

${{ɛ = {1 + \frac{\Delta\; t_{ao}}{{\gamma_{1}T_{a}} - {\Delta\; t_{ao}}}}},{\gamma_{1} > 0},}\;$

it may be calculated as c range that is calculated for values of γ₁range such as

$\frac{\Delta\; t_{a\; 0}}{T_{a}} < \gamma_{1} < {1.25.}$ε_(min_γ) ₂ <ε<ε_(max_γ) ₂   [Conditional Equation 2]

For Conditional Equation 2, in

${ɛ = \frac{{v_{eff}\left( r_{mid} \right)} \cdot T_{obs}}{{{v_{eff}\left( r_{mid} \right)} \cdot T_{obs}} - {\gamma_{2} \cdot \theta_{az} \cdot r_{mid}}}},{\gamma_{2} > 0},$

it may be calculated as ε range that is calculated for values of γ₂range such as

$1 < \gamma_{2} < {0.75 \cdot {\frac{B_{a\_{Targe}t}}{B_{FOV}}.}}$

Here,

$B_{a\_{Target}} = {\frac{2{v_{eff}^{2}\left( r_{mid} \right)}}{\lambda\; r_{mid}} \cdot T_{obs}}$

and T_(obs) is the target observation duration (Target dwell time).

The embodiments described above may be implemented as a hardwarecomponent, a software component, and/or a combination of a hardwarecomponent and a software component. For example, the devices, methods,and components described in the embodiments may be implemented by usingone or more general computer or specific-purpose computer such as aprocessor, a controller, an arithmetic logic unit (ALU), a digitalsignal processor, a microcomputer, a field programmable gate array(FPGA), a programmable logic unit (PLU), a microprocessor, or any otherdevice capable of executing instructions and responding thereto. Theprocessing device may execute an operating system (OS) and one or moresoftware applications executed on the operating system. Further, theprocessing device may access, store, operate, process, and generate datain response to the execution of software. For convenience ofunderstanding, although it may be described that one processing deviceis used, one of ordinary skill in the art may understand that theprocessing device may include a plurality of processing elements and/ora plurality of types of processing elements. For example, the processingdevice may include a plurality of processors or one processor and onecontroller. In addition, other processing configurations such as aparallel processor are possible.

The software may include a computer program, code, instructions, or acombination of one or more of the above, and may configure theprocessing unit, or instruct the processing unit independently orcollectively to operate as desired. Software and/or data may beinterpreted by the processing device or, in order to provideinstructions or data to the processing device, may be embodied in anytype of machine, component, physical device, virtual equipment, computerstorage medium or device, or signal wave transmission, permanently ortemporarily. The software may be distributed over networked computersystems and stored or executed in a distributed manner. The software anddata may be stored on one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form ofprogram instructions that can be executed through various computer meansand recorded in a computer-readable medium. The computer readable mediummay include program instructions, data files, data structures, and thelike alone or in combination. The program instructions recorded on themedium may be those specially designed and configured for the purposesof the embodiments, or may be known and available to those skilled incomputer software. Examples of computer readable recording mediuminclude magnetic media such as hard disks, floppy disks, and magnetictape, optical media such as CD-ROMs and DVDs, magneto-optical media suchas floptical disks, and hardware devices specifically configured tostore and execute program instructions such as ROM, RAM, flash memory,and the like. Examples of the program instructions include machinelanguage codes such as those generated by a compiler, as well ashigh-level language codes that may be executed by a computer using aninterpreter, and so on. The hardware device described above may beconfigured to operate as one or more software modules in order toperform the operations according to the embodiments, and vice versa.

As described above, although the embodiments have been described withreference to the limited drawings, a person of ordinary skill in the artcan apply various technical modifications and variations based on theabove. For example, even when the described techniques are performed inan order different from the described method, and/or even when thecomponents of the system, structure, device, circuit, and the like arecoupled or combined in a form different from the way described, orreplaced or substituted by other components or equivalents, anappropriate result can be achieved.

1. A method for processing synthetic aperture radar image (SAR) rawdata, the method comprising: dividing the SAR raw data into one or moresub-aperture data by a predetermined number in an azimuth direction;performing a spectral length extension fast Fourier transform (FFT) onthe divided sub-aperture data in the azimuth direction; multiplying thedivided sub-aperture data by a chirp scaling function; performing theFFT on the divided sub-aperture data in a range direction; performing arange compression, a secondary range compression (SRC), and a bulk rangecell migration correction (RCMC) on the divided sub-aperture data;performing an inverse chirp-z transform on the divided sub-aperture datain the range direction; multiplying the divided sub-aperture data by afirst function predetermined for residual phase correction and azimuthscaling; performing an inverse fast Fourier transform (IFFT) on thedivided sub-aperture data in the azimuth direction; recombining thedivided sub-aperture data; multiplying the recombined data by a secondfunction to perform deramping; performing the FFT on the recombined datain the azimuth direction; performing the IFFT on the recombined data inthe azimuth direction; multiplying the recombined data by a thirdfunction to perform deramping; performing the FFT on the recombined datain the azimuth direction; performing an azimuth compression bymultiplying the recombined data by a fourth function; performing theinverse chirp-z transform in an azimuth direction on the recombineddata; and multiplying the recombined data by a fifth function for phasepreservation.
 2. (canceled)
 3. (canceled)
 4. (canceled)
 5. (canceled) 6.The method of claim 1, wherein the inverse chirp-z transform isperformed by Equation 1 below:x_(k) = ? ⋅ [FFT{IFFT{Y_(n)} ⋅ IFFT{?}}],                 k = 0, 1, …  , M − 1                            ${Y_{n} = {{{X\left( 2_{n} \right)} \cdot B^{n} \cdot \text{?} \cdot n} = \frac{F_{n}}{\Delta\; F}}}\mspace{410mu}$B = B₀ ⋅ exp (j 2πθ₀) ⋅ θ₀ = Δ F ⋅ t₀                      W = W₀ ⋅ exp (j2 πϕ₀) ⋅ ϕ₀ = Δ F ⋅ Δ t                    ?indicates text missing or illegible when filed where, X(z_(n)) is asignal in the input frequency domain, M is the number of output samplesignals, ΔF is frequency spacings of the input spectrum signals, and B₀and W₀ are amplitude constants, and the start time of the signal x_(k)on the output time is set to t₀, and the time spacing of the samples isset to Δt.
 7. The method of claim 1, wherein the first function isdefined by Equation 2, the second function by Equation 3, the thirdfunction by Equation 4, the fourth function by Equation 5, and the fifthfunction by Equation 6 as follows: $\begin{matrix}{{H_{4}\left( {f_{a},r} \right)} = {{M_{1}\left( w_{\eta} \right)} \cdot {\exp\left\lbrack {{- j}\frac{\pi}{K_{scl}(r)}f_{a}^{2}} \right\rbrack}}} & \left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack\end{matrix}$ where,${{K_{scl}(r)} = {- \frac{2{v_{eff}^{2}(r)}}{\lambda\;{r_{scl}(r)}}}},$r _(scl)(r)=r,M ₁(w _(η))=exp[j{2(2π/λ+w _(r) /c)R _(r2)(η*)+w _(η)η*}],R _(r2)(η)=c ₄η⁴+(c ₃+4c ₄ t ₃)η³+(c ₂+3c ₃ t ₁+6c ₄ t ₁ ²)η² where, λis the wavelength with respect to the center frequency of thetransmission signal forming the beam, r is the closest approach range,f_(a) is the azimuth Frequency (Doppler Frequency shift), v_(eff) is theeffective velocity, K_(scl)(r) is a scaling Doppler rate, and c₂, c₃ andc₄ are coefficients obtained from geometry including orbit informationand attitude information of a satellite;H ₅(t _(a) ,r)=exp[−jπK _(rot_geometry)·(t _(a) −t _(mid))²]  [Equation3] where, t_(a) is the azimuth time, t_(mid) is the azimuth time of theselected derotation center, and r_(rot_geometry) is the distance to thebeam rotation center given by geometry,$K_{{rot}\; 1} = {K_{{rot}\_{geometry}} = {- \frac{2{v_{eff}^{2}(r)}}{\lambda\; r_{{ro}\;{t\_{geomtry}}}}}}$is an azimuth derotation Doppler rate;H ₆(t _(a) ,r)=exp[−jπ(K _(rot2)(r)−K _(rot_geometry))·(t _(a) −t_(mid))²]  [Equation 4] where${K_{{rot}\; 2}(r)} = {- \frac{2{v_{eff}^{2}(r)}}{\lambda\;{r_{{rot}\; 2}(r)}}}$is an azimuth deramping Doppler rate, andr _(rot2)(r)=r·ε; $\begin{matrix}{{{H_{7}\left( {f_{a},r} \right)} = {{W\left( f_{a} \right)} \cdot {\exp\left\lbrack {j\frac{\pi}{K_{eff}(r)}f_{a}^{2}} \right\rbrack}}}{{where},{{{K_{eff}(r)} = {{K_{act}(r)} - {K_{{rot}\; 2}(r)}}};{and}}}} & \left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack \\{{{H_{8}\left( {t_{a},r} \right)} = {\exp\left\lbrack {j\;\pi\;{{K_{t}(r)} \cdot \left( {1 - \frac{1}{ɛ}} \right)^{2} \cdot \left( {t_{a} - t_{mid}} \right)^{2}}} \right\rbrack}}{{where},{{K_{t}(r)} = {- {\frac{2{v_{eff}^{2}(r)}}{\lambda \cdot {r\left( {ɛ - 1} \right)}}.}}}}} & \left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack\end{matrix}$
 8. The method of claim 7, wherein, when the SARoperational mode is Stripmap, ScanSAR and TOPS mode,$ɛ = \frac{r_{{rot}\_{geometry}}}{r_{mid}}$ is set, and, when the SARoperational mode is Stripmap or ScanSAR, if the value ofr_(rot_geometry) cannot be calculated numerically,r_(rot_geometry)=1000·r_(mid), if |r_(rot_geometry)|>1000·r_(mid) andr_(rot_geometry)>0, r_(rot_geometry)=1000·r_(mid), if|r_(rot_geometry)|>1000·r_(mid) and r_(rot_geometry)<0,r_(rot_geometry)=−1000 r_(mid), and if |r_(rot_geometry)|≤1000·r_(mid),the calculated value of r_(rot_geometry) is applied as it is, wherer_(mid) is the closest distance to the center of a scene.
 9. The methodof claim 7, when a SAR operational mode is a spotlight operational modeincluding a sliding spotlight and a staring spotlight, ε is set by thefollowing equation:$ɛ_{optimized} = \frac{ɛ_{{\min{\_\gamma}}_{1},\gamma_{2}} + ɛ_{{\max{\_\gamma}}_{1},\gamma_{2}}}{2}$where ε_(min_γ) ₁ _(,γ) ₂ , ε_(max_γ) ₁ _(,γ) ₂ are defined as an upperlimit and a lower limit of ε that satisfy both Conditional Equation 1and Conditional Equation 2 below: [Conditional Equation 1] ε_(min_γ) ₁<ε<ε_(max_γ) ₁ , wherein, Conditional Equation 1 is calculated as εrange that is calculated for values of γ₁ range such as${{{\frac{\Delta\; t_{a\; 0}}{T_{a}} < \gamma_{1} < {1.25\mspace{14mu}{in}\mspace{14mu} ɛ}} = {1 + \frac{\Delta\; t_{ao}}{{\gamma_{1}T_{a}} - {\Delta\; t_{ao}}}}},{{\gamma_{1} > 0};}}\;$and [Conditional Equation 2] ε_(min_γ) ₁ <ε<ε_(max_γ) ₂ , whereinConditional Equation 2 is calculated as ε range that is calculated forvalues of γ₂ range such as${{1 < \gamma_{2} < {{0.75 \cdot \frac{B_{a\_{Targe}t}}{B_{FOV}}}\mspace{14mu}{in}\mspace{14mu} ɛ}} = \frac{{v_{eff}\left( r_{mid} \right)} \cdot T_{obs}}{{{v_{eff}\left( r_{mid} \right)} \cdot T_{obs}} - {\gamma_{2} \cdot \theta_{az} \cdot r_{mid}}}},{\gamma_{2} > 0},{where},{B_{a\_{Target}} = {\frac{2{v_{eff}^{2}\left( r_{mid} \right)}}{\lambda\; r_{mid}} \cdot T_{obs}}}$and T_(obs) is the target observation duration (Target dwell time). 10.A computer-readable recording medium storing a program for performing amethod for processing a synthetic aperture radar image (SAR) raw data,the program comprising: an instruction set for dividing the SAR raw datainto one or more sub-aperture data by a predetermined number in anazimuth direction; an instruction set for performing a spectral lengthextension fast Fourier transform (FFT) on the divided sub-aperture datain the azimuth direction; an instruction set for multiplying the dividedsub-aperture data by a chirp scaling function; an instruction set forperforming the FFT on the divided sub-aperture data in a rangedirection; an instruction set for performing a range compression, asecondary range compression (SRC), and a bulk range cell migrationcorrection (RCMC) on the divided sub-aperture data; an instruction setfor performing an inverse chirp-z transform on the divided sub-aperturedata in the range direction; an instruction set for multiplying thedivided sub-aperture data by a first function predetermined for residualphase correction and azimuth scaling; an instruction set for performingan inverse fast Fourier transform (IFFT) on the divided sub-aperturedata in the azimuth direction; an instruction set for recombining thedivided sub-aperture data; an instruction set for multiplying therecombined data by a second function to perform deramping; aninstruction set for performing the FFT on the recombined data in theazimuth direction; an instruction set for performing the IFFT on therecombined data in the azimuth direction; an instruction set formultiplying the recombined data by a third function to performderamping; an instruction set for performing the FFT on the recombineddata in the azimuth direction; an instruction set for performing anazimuth compression by multiplying the recombined data by a fourthfunction; an instruction set for performing the inverse chirp-ztransform in an azimuth direction on the recombined data; and aninstruction set for multiplying the recombined data by a fifth functionfor phase preservation.